Voot Tangkaratt
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Explore the profile of Voot Tangkaratt including associated specialties, affiliations and a list of published articles.
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7
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4
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Recent Articles
1.
Osa T, Tangkaratt V, Sugiyama M
Neural Netw
. 2022 May;
152:90-104.
PMID: 35523085
Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse...
2.
Sasaki H, Tangkaratt V, Niu G, Sugiyama M
Neural Comput
. 2017 Nov;
30(2):477-504.
PMID: 29162006
Sufficient dimension reduction (SDR) is aimed at obtaining the low-rank projection matrix in the input space such that information about output data is maximally preserved. Among various approaches to SDR,...
3.
Tangkaratt V, Sasaki H, Sugiyama M
Neural Comput
. 2017 Jun;
29(8):2076-2122.
PMID: 28599116
A typical goal of linear-supervised dimension reduction is to find a low-dimensional subspace of the input space such that the projected input variables preserve maximal information about the output variables....
4.
Tangkaratt V, Morimoto J, Sugiyama M
Neural Netw
. 2016 Sep;
84:1-16.
PMID: 27639719
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model...
5.
Tangkaratt V, Xie N, Sugiyama M
Neural Comput
. 2014 Nov;
27(1):228-54.
PMID: 25380340
Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case,...
6.
Tangkaratt V, Mori S, Zhao T, Morimoto J, Sugiyama M
Neural Netw
. 2014 Jul;
57:128-40.
PMID: 24995917
The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL...
7.
Zhao T, Hachiya H, Tangkaratt V, Morimoto J, Sugiyama M
Neural Comput
. 2013 Mar;
25(6):1512-47.
PMID: 23517103
The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge is how to reduce the...